Big data analytics for the prediction of tourist preferences worldwide / N. Padmaja, Rajalakshmi Subramaniam, and Sanjay Mohapatra.

Format:

Book

Author/Creator:

Padmaja, N., author.

Subramaniam, Rajalakshmi, author.

Mohapatra, Sanjay, author.

Series:

Emerald points.

Emerald Points Series

Language:

English

Subjects (All):

Big data.

Physical Description:

1 online resource (145 pages)

Edition:

First edition.

Place of Publication:

Leeds, England : Emerald Publishing Limited, [2024]

Summary:

Big Data Analytics for the Prediction of Tourist Preferences Worldwideexplores the benefits, importance and demonstrates how Big Data can be applied in predicting tourist preferences and delivering tourism services in a customer friendly manner.

Contents:

Cover

Big Data Analytics for the Prediction of Tourist Preferences Worldwide

Copyright Page

Contents

List of Figures and Tables

List of Abbreviations

Preface

1. Introduction

1.1 Big Data Analytics in Tourism Sector

1.2 Problem Statement

1.3 Objectives

1.4 Research Contributions

1.5 Chapters in the Book

Chapter 1: Introduction

Chapter 2: Literature Review

Chapter 3: Design of the Proposed System

Chapter 4: Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Algorithm

Chapter 5: Predicting Hotel Preferences of International and Domestic Tourists Using Pointwise Mutual Information

Chapter 6: Big Data Analytics in Predicting Tourist Preferences Based on Hotel Ratings Using Multiclass Multilabel Classifi ...

Chapter 7: Performance Evaluation

Chapter 8: Discussion and Conclusion

1.6 Summary

2. Literature Review

2.1 Introduction

2.2 Definition of Big Data Analytics

2.3 Purpose of Big Data Analytics in Tourism Sector

2.4 Benefits of Big Data in Tourism Sector

2.5 Challenges of Big Data in the Tourism Sector

2.6 Application of Big Data in the Tourism Sector

2.7 Research Gap

2.8 Summary

3. Design of the Proposed System

3.1 Introduction

3.2 Description of the Proposed System

Step 1: Data Collection

Step 2: Apply Part of Speech Tagging

Step 3: Estimate Occurrence Frequency

Step 4: Estimate Pointwise Mutual Information (PMI)

Step 5: Generate Output Result

Step 6: Construct a Gold List

Step 7: Vectorized and Labelled

Step 8: Mapping Is Performed

Step 9: Performance Evaluation

Step 10: Compute Accuracy

3.3 Data Set Description

3.4 Implementation of the System

3.5 Summary.

4. Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Algorithm

4.1 Introduction

4.2 Proposed Predicting Preferences of International and Domestic Tourists Using Association Rule Mining System

Step 1: Collect Data

Step 2: Prepare Data

Step 3: Review Data Set Through Association Rule Mining

Support

Confidence

Step 4: Classification and Results

4.3 Discussion and Results

4.3.1 Discussion

4.3.2 Results

4.3.2.1 Domestic City

4.3.2.2 Features of International Cities

4.3.3 Implementation of the Result

4.3.3.1 Features of New Delhi Hotels

4.3.3.2 Features of Beijing Hotels

4.3.3.3 Features of Chicago Hotels

4.3.3.4 Features of Dubai Hotels

4.3.3.5 Features of London Hotels

4.3.3.6 Features of Montreal Hotels

4.3.3.7 Features of New York Hotels

4.3.3.8 Features of San Francisco Hotels

4.3.3.9 Features of Shanghai Hotels

4.3.3.10 International Tourism of Vegas Hotels

4.4 Summary

5. Predicting Hotel Preferences of International and Domestic Tourists Using Pointwise Mutual Information

5.1 Introduction

5.2 Overview of Opinion Mining

5.3 PMI in Tourism

5.4 Proposed Opinion Mining Using PMI

Step 2: Apply Part of Speech (POS) Tagging

Step 3: Calculate Occurrence Frequency

Step 4: Calculate PMI

5.5 Results and Discussion

5.5.1 Beijing

5.5.2 Chicago

5.5.3 Dubai

5.5.4 Las Vegas

5.5.5 London

5.5.6 Montreal

5.5.7 New Delhi

5.5.8 San Francisco

5.5.9 Shanghai

5.5.10 Comparison of the Results of the Proposed System

5.6 Summary

6. Big Data Analytics in Predicting Tourist Preferences Based on Hotel Ratings Using Multiclass Multilabel Classification A ...

6.1 Introduction.

6.2 Importance of Multiclass Multilabel Classification in the Tourism Sector

6.3 Proposed System Multiclass Multilabel Classification in the Tourism Sector

Step 1: Construct a Gold List

Step 2: Vectorized and Labelled

Step 3: Mapping Is Performed

Algorithms Used

TF-IDF

LDA Topic Modelling

Doc2Vec

Step 4: Compute Accuracy

6.4 Discussion and Results

6.4.1 Discussion

6.4.2 Results of Proposed System

6.4.2.1 Beijing

6.4.2.2 Chicago

6.4.2.3 Dubai

6.4.2.4 Las Vegas

6.4.2.5 London

6.4.2.6 Montreal

6.4.2.7 New Delhi

6.4.2.8 New York

6.4.2.9 San Francisco

6.4.2.10 Shanghai

6.4.3 Constructing Gold List of Features

Adding List of Features to Each Hotel

6.4.4 Topic Modelling

6.4.5 Doc2Vec

6.4.6 TF-IDF Features

6.4.7 Features of All Cities

6.5 Summary

7. Performance Evaluation

7.1 Term Frequency-Inverse Document Frequency

7.2 Latent Dirichlet Allocation

7.3 Doc2Vec

7.4 Testing and Training With LDA Topic Modelling

7.5 Testing With Doc2Vec

7.6 Testing With TF-IDF Features

7.7 Accuracy Comparison of TF-IDF, LDA Topic Modelling and Doc2Vec

7.8 Conclusion

8. Discussion and Conclusion

8.1 Summary of the Findings of the Research

8.2 Benefits and Importance of Big Data Analytics in Tourism Industry

8.3 Conclusion

8.4 Implications and Future Research

References.

Notes:

Includes bibliographical references.

Description based on print version record.

ISBN:

1-83549-338-6